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Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

ABSTRACT: OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD treatment outcome could enable treatment optimization leading to better outcomes. To that end, the authors applied image-based computational analysis to clinically FD-treated aneurysms to extract information regarding morphology, pre- and post-treatment hemodynamics, and FD-device characteristics and then used these parameters to train machine learning algorithms to predict 6-month clinical outcomes after FD treatment.METHODSData were retrospectively collected for 84 FD-treated sidewall aneurysms in 80 patients. Based on 6-month angiographic outcomes, IAs were classified as occluded (n = 63) or residual (incomplete occlusion, n = 21). For each case, the authors modeled FD deployment using a fast virtual stenting algorithm and hemodynamics using image-based computational fluid dynamics. Sixteen morphological, hemodynamic, and FD-based parameters were calculated for each aneurysm. Aneurysms were randomly assigned to a training or testing cohort in approximately a 3:1 ratio. The Student t-test and Mann-Whitney U-test were performed on data from the training cohort to identify significant parameters distinguishing the occluded from residual groups. Predictive models were trained using 4 types of supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM; linear and Gaussian kernels), K-nearest neighbor, and neural network (NN). In the testing cohort, the authors compared outcome prediction by each model trained using all parameters versus only the significant parameters.RESULTSThe training cohort (n = 64) consisted of 48 occluded and 16 residual aneurysms and the testing cohort (n = 20) consisted of 15 occluded and 5 residual aneurysms. Significance tests yielded 2 morphological (ostium ratio and neck ratio) and 3 hemodynamic (pre-treatment inflow rate, post-treatment inflow rate, and post-treatment aneurysm averaged velocity) discriminants between the occluded (good-outcome) and the residual (bad-outcome) group. In both training and testing, all the models trained using all 16 parameters performed better than all the models trained using only the 5 significant parameters. Among the all-parameter models, NN (AUC = 0.967) performed the best during training, followed by LR and linear SVM (AUC = 0.941 and 0.914, respectively). During testing, NN and Gaussian-SVM models had the highest accuracy (90%) in predicting occlusion outcome.CONCLUSIONSNN and Gaussian-SVM models incorporating all 16 morphological, hemodynamic, and FD-related parameters predicted 6-month occlusion outcome of FD treatment with 90% accuracy. More robust models using the computational workflow and machine learning could be trained on larger patient databases toward clinical use in patient-specific treatment planning and optimization.

SUBMITTER: Paliwal N

PROVIDER: S-EPMC6421840 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

Paliwal Nikhil N Jaiswal Prakhar P Tutino Vincent M VM Shallwani Hussain H Davies Jason M JM Siddiqui Adnan H AH Rai Rahul R Meng Hui H

Neurosurgical focus 20181101 5

OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD treatment outcome could enable treatment optimization leading to better outcomes. To that end, the authors applied image-based computational analysis to clinically FD-treated aneurysms to extract information regarding morphology, pre- and post-treatment ...[more]

PMID: 30453461

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Project description:BACKGROUND AND PURPOSE:Incompletely occluded flow diverter treated aneurysms remain at risk of rupture and thromboembolic complications. Our aim was to identify the potential for incomplete occlusion of intracranial aneurysms treated by flow diverters. We investigated whether aneurysm ostium size in relation to parent artery size affects angiographic outcomes of flow diverter-treated sidewall aneurysms. MATERIALS AND METHODS:Flow diverter-treated sidewall aneurysms were divided into "occluded" and "residual" (incomplete occlusion) groups based on 6-month angiographic follow-up. We calculated the ostium ratio, a new parameter defined as the aneurysm ostium surface area versus the circumferential surface area of the parent artery. We also calculated the neck ratio, defined as clinical aneurysm neck diameter versus parent artery diameter from pretreatment 2D DSA, as a 2D surrogate. We compared the performance of these ratios with existing aneurysm morphometrics (size, neck diameter, volume, aspect ratio, size ratio, undulation index, nonsphericity index, ellipticity index, bottleneck factor, aneurysm angle, and parent vessel angle) and flow diverter-related parameters (metal coverage rate and pore density). Statistical tests and receiver operating characteristic analyses were performed to identify significantly different parameters between the 2 groups and test their predictive performances. RESULTS:We included 63 flow diverter-treated aneurysms, 46 occluded and 17 residual. The ostium ratio and neck ratio were significantly higher in the residual group than in the occluded group (P < .001 and P = .02, respectively), whereas all other parameters showed no statistical difference. As discriminating parameters for occlusion, ostium ratio and neck ratio achieved areas under the curve of 0.912 (95% CI, 0.838-0.985) and 0.707 (95% CI, 0.558-0.856), respectively. CONCLUSIONS:High ostium ratios and neck ratios could predict incomplete occlusion of flow diverter-treated sidewall aneurysms. Neck ratio can be easily calculated by interventionists to predict flow-diverter treatment outcomes.

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Project description:BackgroundRepeated invasive intracranial pressure (ICP) monitoring is desirable because many neurosurgical pathologies are associated with elevated ICP. On the other hand, it could become a risk for children to repeat sedation, anesthesia, or radiation exposure. As a non-invasive method, measurements of optic nerve sheath diameter (ONSD) has been revealed to accurately predict increased ICP. However, no studies have indicated a relationship among age, brain, and ventricular parameters in normal children, nor a prediction of increased ICP with artificial intelligence.Methods and findingsThis study enrolled 400 normal children for control and 75 children with signs of increased ICP between 2009 and 2019. Measurements of the parameters including ONSD on CT were obtained. A supervised machine learning was applied to predict suspected increased ICP based on CT measurements. A linear correlation was shown between ln(age) and mean ONSD (mONSD) in normal children, revealing mONSD = 0.36ln(age)+2.26 (R2 = 0.60). This study revealed a linear correlation of mONSD measured on CT with ln(age) and the width of the brain, not the width of the ventricles in 400 normal children based on the univariate analyses. Additionally, the multivariate analyses revealed minimum bicaudate nuclei distance was also associated with mONSD. The results of the group comparison between control and suspected increased ICP revealed a statistical significance in mONSD and the width of the ventricles. The study indicated that supervised machine learning application could be applied to predict suspected increased ICP in children, with an accuracy of 94% for training, 91% for test.ConclusionsThis study clarified three issues regarding ONSD and ICP. Mean ONSD measured on CT was correlated with ln(age) and the width of the brain, not the width of the ventricles in 400 normal children based on the univariate analyses. The multivariate analyses revealed minimum bicaudate nuclei distance was also associated with mONSD. Mean ONSD and the width of ventricles were statistically significant in children with signs of elevated ICP. Finally, the study showed that machine learning could be used to predict children with suspected increased ICP.

Dataset Information

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

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Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

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